from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 2s
KMeans_short: 0h 0m 3s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 11s
LogisticRegression: 0h 0m 21s
KMeans_tall: 0h 0m 25s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 29s
KNeighborsClassifier_kd_tree: 0h 2m 45s
daal4py_KNeighborsClassifier: 0h 2m 48s
xgboost: 0h 5m 9s
catboost_symmetric: 0h 5m 9s
lightgbm: 0h 5m 10s
HistGradientBoostingClassifier: 0h 5m 13s
catboost_lossguide: 0h 5m 49s
KNeighborsClassifier: 0h 34m 8s
total: 1h 8m 2s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.21.0",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.5",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.749 | 0.000 | 1 | 100 | NaN | NaN | 0.502 | 0.000 | 0.277 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.023 | 0.311 | 0.000 | 0.023 | 1 | 100 | 0.934 | 0.716 | 1.999 | 0.054 | 11.517 | 0.347 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.211 | 0.005 | 0.000 | 0.211 | 1 | 100 | 0.000 | 1.000 | 0.086 | 0.002 | 2.460 | 0.078 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.184 | 0.000 | -1 | 1 | NaN | NaN | 0.483 | 0.000 | 0.268 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 25.107 | 0.430 | 0.000 | 0.025 | -1 | 1 | 0.710 | 0.819 | 1.981 | 0.032 | 12.676 | 0.297 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.198 | 0.026 | 0.000 | 0.198 | -1 | 1 | 0.000 | 0.000 | 0.086 | 0.002 | 2.299 | 0.308 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.137 | 0.000 | 5.825 | 0.000 | 1 | 5 | NaN | NaN | 0.505 | 0.000 | 0.272 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 23.133 | 0.224 | 0.000 | 0.023 | 1 | 5 | 0.803 | 0.819 | 2.058 | 0.031 | 11.243 | 0.200 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.213 | 0.002 | 0.000 | 0.213 | 1 | 5 | 1.000 | 0.000 | 0.086 | 0.002 | 2.473 | 0.052 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.153 | 0.000 | -1 | 5 | NaN | NaN | 0.484 | 0.000 | 0.268 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 35.087 | 0.000 | 0.000 | 0.035 | -1 | 5 | 0.803 | 0.936 | 2.067 | 0.047 | 16.973 | 0.382 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.201 | 0.023 | 0.000 | 0.201 | -1 | 5 | 1.000 | 1.000 | 0.085 | 0.001 | 2.370 | 0.276 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.129 | 0.000 | 6.219 | 0.000 | 1 | 1 | NaN | NaN | 0.483 | 0.000 | 0.266 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.360 | 0.046 | 0.000 | 0.013 | 1 | 1 | 0.710 | 0.716 | 1.952 | 0.018 | 6.845 | 0.066 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.206 | 0.004 | 0.000 | 0.206 | 1 | 1 | 0.000 | 1.000 | 0.086 | 0.002 | 2.380 | 0.070 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.132 | 0.000 | 6.064 | 0.000 | -1 | 100 | NaN | NaN | 0.487 | 0.000 | 0.271 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 34.858 | 0.000 | 0.000 | 0.035 | -1 | 100 | 0.934 | 0.936 | 2.040 | 0.015 | 17.089 | 0.126 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.206 | 0.019 | 0.000 | 0.206 | -1 | 100 | 0.000 | 1.000 | 0.087 | 0.002 | 2.362 | 0.221 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.063 | 0.000 | 0.254 | 0.000 | 1 | 100 | NaN | NaN | 0.111 | 0.000 | 0.569 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.571 | 0.151 | 0.000 | 0.021 | 1 | 100 | 0.993 | 0.976 | 0.301 | 0.006 | 68.365 | 1.376 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.001 | 0.000 | 0.030 | 1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 5.126 | 0.349 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.267 | 0.000 | -1 | 1 | NaN | NaN | 0.114 | 0.000 | 0.528 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 22.343 | 0.142 | 0.000 | 0.022 | -1 | 1 | 0.982 | 0.981 | 0.303 | 0.005 | 73.801 | 1.347 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.021 | 0.002 | 0.000 | 0.021 | -1 | 1 | 1.000 | 1.000 | 0.006 | 0.000 | 3.552 | 0.455 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.062 | 0.000 | 0.260 | 0.000 | 1 | 5 | NaN | NaN | 0.114 | 0.000 | 0.539 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 20.289 | 0.363 | 0.000 | 0.020 | 1 | 5 | 0.991 | 0.981 | 0.304 | 0.005 | 66.635 | 1.581 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.030 | 0.001 | 0.000 | 0.030 | 1 | 5 | 1.000 | 1.000 | 0.006 | 0.001 | 5.160 | 0.529 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.070 | 0.000 | 0.227 | 0.000 | -1 | 5 | NaN | NaN | 0.112 | 0.000 | 0.630 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.752 | 0.000 | 0.000 | 0.032 | -1 | 5 | 0.991 | 0.981 | 0.363 | 0.009 | 87.431 | 2.206 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.038 | 0.004 | 0.000 | 0.038 | -1 | 5 | 1.000 | 1.000 | 0.006 | 0.000 | 6.408 | 0.752 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.060 | 0.000 | 0.265 | 0.000 | 1 | 1 | NaN | NaN | 0.116 | 0.000 | 0.523 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.333 | 0.085 | 0.000 | 0.010 | 1 | 1 | 0.982 | 0.976 | 0.309 | 0.006 | 33.453 | 0.670 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.006 | 0.001 | 2.368 | 0.229 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.063 | 0.000 | 0.254 | 0.000 | -1 | 100 | NaN | NaN | 0.117 | 0.000 | 0.537 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 31.734 | 0.000 | 0.000 | 0.032 | -1 | 100 | 0.993 | 0.981 | 0.362 | 0.005 | 87.594 | 1.315 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.037 | 0.003 | 0.000 | 0.037 | -1 | 100 | 1.000 | 1.000 | 0.006 | 0.000 | 6.157 | 0.668 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.273 | 0.000 | 0.024 | 0.000 | 1 | 1 | NaN | NaN | 0.742 | 0.000 | 4.409 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.762 | 0.006 | 0.000 | 0.001 | 1 | 1 | 0.955 | 0.972 | 0.194 | 0.001 | 3.924 | 0.040 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 3.296 | 1.508 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.221 | 0.000 | 0.025 | 0.000 | -1 | 5 | NaN | NaN | 0.755 | 0.000 | 4.267 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.856 | 0.011 | 0.000 | 0.001 | -1 | 5 | 0.966 | 0.965 | 0.108 | 0.002 | 7.950 | 0.163 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 12.210 | 5.251 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.249 | 0.000 | 0.025 | 0.000 | 1 | 5 | NaN | NaN | 0.730 | 0.000 | 4.452 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.468 | 0.004 | 0.000 | 0.001 | 1 | 5 | 0.966 | 0.973 | 0.578 | 0.006 | 2.539 | 0.026 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 2.428 | 0.944 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.169 | 0.000 | 0.025 | 0.000 | -1 | 100 | NaN | NaN | 0.759 | 0.000 | 4.175 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.884 | 0.021 | 0.000 | 0.003 | -1 | 100 | 0.966 | 0.965 | 0.109 | 0.001 | 26.412 | 0.334 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.009 | 0.001 | 0.000 | 0.009 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 29.687 | 14.637 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.239 | 0.000 | 0.025 | 0.000 | 1 | 100 | NaN | NaN | 0.728 | 0.000 | 4.450 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.932 | 0.087 | 0.000 | 0.005 | 1 | 100 | 0.966 | 0.972 | 0.194 | 0.004 | 25.474 | 0.671 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.001 | 0.000 | 0.006 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 14.004 | 6.497 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 3.185 | 0.000 | 0.025 | 0.000 | -1 | 1 | NaN | NaN | 0.742 | 0.000 | 4.291 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.451 | 0.005 | 0.000 | 0.000 | -1 | 1 | 0.955 | 0.973 | 0.578 | 0.009 | 0.781 | 0.015 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 4.355 | 1.916 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.856 | 0.000 | 0.019 | 0.000 | 1 | 1 | NaN | NaN | 0.499 | 0.000 | 1.715 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.975 | 0.984 | 0.001 | 0.000 | 19.201 | 5.807 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.563 | 3.857 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.865 | 0.000 | 0.018 | 0.000 | -1 | 5 | NaN | NaN | 0.511 | 0.000 | 1.694 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.987 | 0.968 | 0.001 | 0.000 | 30.964 | 10.894 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 23.020 | 17.766 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.871 | 0.000 | 0.018 | 0.000 | 1 | 5 | NaN | NaN | 0.510 | 0.000 | 1.708 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.987 | 0.984 | 0.007 | 0.001 | 3.238 | 0.419 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 3.859 | 3.491 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.879 | 0.000 | 0.018 | 0.000 | -1 | 100 | NaN | NaN | 0.516 | 0.000 | 1.704 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.048 | 0.001 | 0.000 | 0.000 | -1 | 100 | 0.985 | 0.968 | 0.001 | 0.001 | 46.163 | 31.122 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 24.484 | 17.712 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.871 | 0.000 | 0.018 | 0.000 | 1 | 100 | NaN | NaN | 0.515 | 0.000 | 1.692 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.057 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.985 | 0.984 | 0.001 | 0.000 | 49.098 | 13.614 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.947 | 3.970 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.881 | 0.000 | 0.018 | 0.000 | -1 | 1 | NaN | NaN | 0.513 | 0.000 | 1.718 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.025 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.975 | 0.984 | 0.011 | 0.004 | 2.221 | 0.816 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 19.286 | 14.069 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.683 | 0.000 | 0.703 | 0.000 | random | NaN | 30 | NaN | 0.509 | 0.0 | 1.341 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.000 | 0.326 | 0.000 | random | 0.000 | 30 | 0.001 | 0.000 | 0.0 | 6.831 | 3.066 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.866 | 6.487 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.623 | 0.000 | 0.770 | 0.000 | k-means++ | NaN | 30 | NaN | 0.450 | 0.0 | 1.385 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.002 | 0.000 | 0.314 | 0.000 | k-means++ | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.691 | 5.956 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.000 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 11.023 | 7.035 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 7.149 | 0.000 | 3.357 | 0.000 | random | NaN | 30 | NaN | 3.097 | 0.0 | 2.308 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.001 | 10.593 | 0.000 | random | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 8.090 | 5.465 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.001 | 0.015 | 0.002 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.872 | 6.854 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.856 | 0.000 | 3.501 | 0.000 | k-means++ | NaN | 30 | NaN | 2.896 | 0.0 | 2.368 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.000 | 13.090 | 0.000 | k-means++ | 0.002 | 30 | 0.002 | 0.000 | 0.0 | 6.054 | 2.111 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.002 | 0.000 | 0.016 | 0.002 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.932 | 6.219 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.254 | 0.0 | 0.013 | 0.000 | k-means++ | NaN | 20 | NaN | 0.103 | 0.0 | 2.471 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.160 | 0.000 | k-means++ | 0.001 | 20 | -0.002 | 0.001 | 0.0 | 2.968 | 0.634 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.606 | 6.597 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.097 | 0.0 | 0.033 | 0.000 | random | NaN | 20 | NaN | 0.036 | 0.0 | 2.677 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.158 | 0.000 | random | 0.000 | 20 | 0.002 | 0.001 | 0.0 | 2.556 | 0.917 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 9.544 | 5.849 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.653 | 0.0 | 0.245 | 0.000 | k-means++ | NaN | 20 | NaN | 0.388 | 0.0 | 1.683 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 5.903 | 0.000 | k-means++ | 0.296 | 20 | 0.322 | 0.001 | 0.0 | 2.153 | 0.331 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.010 | 0.002 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.665 | 4.095 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.238 | 0.0 | 0.673 | 0.000 | random | NaN | 20 | NaN | 0.152 | 0.0 | 1.563 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.003 | 0.0 | 5.805 | 0.000 | random | 0.318 | 20 | 0.322 | 0.001 | 0.0 | 2.115 | 0.339 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.002 | 0.0 | 0.010 | 0.002 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.506 | 3.566 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 12.331 | 0.0 | [-0.09568393] | 0.000 | NaN | NaN | NaN | NaN | NaN | 1.961 | 0.0 | 6.288 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [50.26502232] | 0.000 | NaN | NaN | NaN | NaN | 0.51 | 0.000 | 0.0 | 0.764 | 0.358 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.2382704] | 0.000 | NaN | NaN | NaN | NaN | 1.00 | 0.000 | 0.0 | 0.359 | 0.322 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [26] | 0.894 | 0.0 | [2.32663709] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.739 | 0.0 | 1.210 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [26] | 0.002 | 0.0 | [112.34882669] | 0.000 | NaN | NaN | NaN | NaN | 0.28 | 0.003 | 0.0 | 0.538 | 0.105 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [26] | 0.000 | 0.0 | [19.323492] | 0.000 | NaN | NaN | NaN | NaN | 1.00 | 0.001 | 0.0 | 0.140 | 0.094 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.190 | 0.0 | 0.421 | 0.0 | NaN | NaN | NaN | 0.189 | 0.0 | 1.006 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.010 | 0.0 | 7.982 | 0.0 | NaN | NaN | 0.104 | 0.017 | 0.0 | 0.588 | 0.023 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.0 | 0.930 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.641 | 0.574 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.556 | 0.0 | 0.514 | 0.0 | NaN | NaN | NaN | 0.243 | 0.0 | 6.407 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.0 | 5.344 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.648 | 0.453 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.0 | 0.010 | 0.0 | NaN | NaN | NaN | 0.000 | 0.0 | 0.660 | 0.561 | See | See |